RRX Pharma-Bioinformatics Future Challenges in Bioinformatics

Future Challenges in Bioinformatics

RRXPharma

RRXPharma

Introduction

• Introduction: How RRX got involved …

• Life sciences context: How bioinformatics came to be important…

• The past half century: How bioinformatics has “evolved”…

Introduction

• Categories of Bioinformatics Tools

• Why We Need Supercomputers

• Software Development Issues

• Future Challenges

• Tools for Biotech Projects

• Summary

How RRX got involved …

• Submitted a Canadian Foundation for Innovation (CFI) proposal for Advanced Bioinformatics Collaborative Computing (ABioCC)

How RRX got involved …

• Developed an SVG based visualization front end

• Paper will be presented at SVG Open 2003 in Vancouver on July 17th

How bioinformatics came to be important…

• After the structure of DNA was reverse engineered with X-Ray diffraction in 1953 focus shifted to nucleic acid sequence analysis

• DNA/RNA/protein sequence data accumulated using computer programs for storage and analysis


• Bioinformatics algorithms in development for the last half century came into wide spread use by researchers

• The ability to compare sequences created a homology context for unknown sequences of interest leading to advances…


• Improved sequencing technology enabled the complete deciphering of the human genome >>> 1999

• About 3.18 billion base pairs

• Celera used 300 PE Biosystems ABI Prism 3700 DNA Analysers

How bioinformatics has “evolved”…

• Central dogma of molecular biology – – DNA sequences are transcribed into mRNA

sequences, mRNA sequences are translated into protein sequences, which fold 3D creating structures with functions statistically survival selected >>> affecting the prevalence of the underlying DNA sequences in a population


• This created a supporting information flow– Organization and control of genes in the DNA

sequence– Identification of transcriptional units in the

DNA sequence– Prediction of protein structure from sequence– Analysis of molecular function


• Another covariant information flow was created based on the scientific method– Create hypothesis wrt biological activity– Design experiments to test the hypothesis– Evaluate resulting data for compatibility with

the hypothesis– Extend/modify hypothesis in response


• IT used to handle explosion of data from high throughput techniques, too complex for manual analysis– X-ray diffraction


– Automated DNA sequencing• Amersham Biosciences• Applied Biosystems• Beckman Coulter• LI-COR• SpectruMedix Corp.• Visible Genetics Corp.


– Microarray expression analysis


• Rapid emergence of 3D macromolecular structure databases– New sub discipline: structural

bioinformatics• Atomic and sub cellular spatial

scales– Representation/physics– Storage/retrieval/source data

correlation/interpretation– Analysis/simulation– Display/visualization


Categories of Bioinformatics Tools…

• Databases >>> search/compare• Sequence Analysis - Clusters• Genomics• Phylogenics• Structure Prediction• Molecular Modelling• Microarrays• Packages, Misc Apps, Graphics, Scripts

• aceperl

• BLAST

• Blastall

• Blastpgp

• BLAT

• Blimps

• Entrez

• FASTA

• fastacmd

• formatdb

• getz

• HMMER

• IMPALA

• InterProScan

• PHI-BLAST

• ProSearch

• PSI-BLAST

• PSI-BLASTN

• Seguin

• Swat

• tace

• xace

Categories of Bioinformatics Tools…•Database >>> search/compare

Sequence Analysis• Artemis

• Bl2seq

• BLAST

• Clustal W, X

• consed/autofinish

• Cross_match

• Dotter

• EMBOSS

• FASTA

• Glimmer

• HMMER

• InterProScan

• MEME

• View

• Paracel Transcript Assem

• Phrap

• Phred

• Primers

• ProSearch

• Readseq2

• Rnabob

• RRTree

• SAPS

• seals

• Seqsblast

• STADEN

• Swat

• T-Coffee

Genomics• Calc_primers• Cross_match• FPC• GENSCAN• Glimmer• Image• Mzef• Phrap

• Phred• STADEN• Swat• tace• tace_celegans• tRNAscan-SE• xace• xace_celegans

Phylogenics

• Clustal W• Clustal X• MOLPHY• MrBayes• PHYLIP

• RRTree• T-Coffee• TREE-PUZZLE• TreeViewX

Structure Prediction

• EMBOSS

• MEME

• Modeller

• Mzef

• PHI-BLAST

Molecular Modelling

• Modeller– homology modeling an alignment of a sequence to be

modeled with known related structures

• Rasmol– a molecular graphics program intended for 3D

visualisation of proteins and nucleic acids

• Raster3D (publishing images)

• X3DNA– analyzing and rebuilding 3D structures

Microarrays

• Dapple– a program for quantitating spots on a two-

colour DNA microarray image..

• OligoArray– a program that computes gene specific

oligonucleotides that are free of secondary structure for genome-scale oligonucleotide microarray construction.

Packages, Useful Scripts/Source Code, Graphics, PERL

• BioPERL• BioJava• boxshade• mvscf• seg• Split_fasta

• povRay• Raster3D• MOLPHY

Why We Need Supercomputers…

• Some commercial packages run on “supercomputers”– Accelrys: modeling and simulation

• Materials Studio• Cerius2 (SGI Unix only)

– Homology modeling to catalyst design

• Insight II (SGI Unix only)– 3D graphical environment for physics based molecular

modeling

• Catalyst (high end Unix servers)– database management valuable in drug discovery

research

• QUANTA (high end Unix servers)– crystallographic 2D/3D protein structure solution

• Discovery Studio


• Supercomputer advantages– Multiple processors

– Large shared memory

– Handle very large files

– Large/fast RAID arrays

– Terabyte tape backup systems

– Power backup systems

– High performance networks


• Common bioinformatics requirements– Computationally intensive tasks– Large memory models– Intensive/complex database searches– Large experimental database sets– Large derived database sets– Large persistent intermediate data structures– Teamwork data sharing and visualization


• Network requirements– Driving gigE/10gigE NICs

• Moving large files/data sets rapidly

• Visualization streams/Access GRID

• Coordinating Cluster/GRID computing

• Dynamic provisioning of light paths


OS

R es ea rchOb je ct s

Mul ti ca st OB GP MP LS IPv 6 GR ID

Object Messaging System

HTTPSession Operator Session Memory Model Beans

Java Server Page Handler

Java ServeletHandler

HostJava VM(s)

Data BaseServer(s)

DAO

OS

IRR Server Wrapper

IRR

IRR

IRR

IRR


Request Analyzer

Server BeanCacheResearch

Handler

RtConfig Handler

Whois Handler

GRIDCluster

GRID Cluster Network Object Model


GRID Resource Coordination Model

N x NOXC

Router OS

OXC MIB(s)/Session(s)Device Config File(s)

ResearchLab

ResearchLab

Collaboration Object Models

Configuration

Why We Need Supercomputers… xxxxxxxxxxxxxxxxxxxxxxx

Software Development Issues…

• Collaboration contexts/barriers – Team work … collaboration spaces– Standards development … DTDs– Integration issues…

• experimental data to homology to 3D model

• platform issues…

• network issues – 9k MTU - jumbo frames

– Licensing issues – public vs. private

Future Challenges…

• Creating developer infrastructure for building up structural models from component parts …– components from macromolecule libraries ported

to object models

• Understanding the design principles of systems of macromolecules and harnessing them to create new functions …– specialized molecular machines

Future Challenges…

• Learning to design drugs efficiently and cost effectively based on knowledge of the target …– target generation automation– validation automation

• Development of enhanced simulation models that give insight into context based function from knowledge of structure …– possible use of artificial intelligence to limit

scope of search

How Tools might be used for Industry Biotech Projects

Summary

• Bioinformatics – well positioned to assist with

application development– exploring novel bioinformatics

software development– proceeding with supporting access

GRID and optical switching technology

Questions/Comments… ? ;-)

Documents

RRX Pharma-Bioinformatics Future Challenges in Bioinformatics